Traditional Chinese medicine formulae system based on multi-omics integrated analysis of extracellular vesicles

Abstract

The application provides a traditional Chinese medicine classical prescription system for analyzing extracellular vesicles based on multi-omics integration, comprising: a data acquisition module for acquiring biopsy sample information of different groups of individuals based on related experiments and literature of traditional Chinese medicine classical prescription treatment; a data processing module for obtaining and preprocessing extracellular vesicle data and intestinal flora data based on the biopsy sample information; a multi-omics integration module for obtaining multi-omics integration data; a discovery and verification module for obtaining potential classical prescription efficacy evaluation biomarkers and experimentally verifying the potential classical prescription efficacy evaluation biomarkers to obtain verification results; and a systems biology application module for obtaining the regulation mechanism of extracellular vesicles in a biological system based on the construction of a gene-protein-metabolite interaction network. The application can comprehensively reveal the efficacy mechanism of traditional Chinese medicine classical prescriptions and provide strong technical support and application prospects for the modernization, scientification and internationalization development of traditional Chinese medicines.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of traditional Chinese medicine research and application, and specifically relates to a traditional Chinese medicine classical prescription system based on multi-omics integrated analysis of extracellular vesicles. BACKGROUND

[0002] Traditional Chinese medicine classical prescriptions are an important part of the inheritance and development of traditional Chinese medicine, with a long history and rich clinical experience. Classical prescriptions, also known as classical formulas, are prescriptions that are recorded in ancient classical works of traditional Chinese medicine and have been tested by long-term clinical practice, with significant efficacy and wide application. These classical prescriptions play an important role in the theoretical system of traditional Chinese medicine, and are characterized by rigorous prescription, simple medicinal ingredients, and definite efficacy, i.e., "universal, simple, inexpensive, and effective", which embodies the essence of traditional Chinese medicine syndrome differentiation and treatment. The use of classical prescriptions indeed has its unique advantages. If accurate differentiation is made and appropriate classical prescriptions are selected, rapid effects can often be achieved in the treatment process. The comprehensive results of the multi-component and multi-target effects of classical prescription drugs are closely related to the holistic concept of traditional Chinese medicine treatment and the principle of individualized treatment. The development of modern scientific and technological methods provides new means and methods for the study of classical prescriptions, especially through high-throughput omics technology and systems biology methods, which can more deeply explore the pharmacodynamic mechanisms and action pathways of classical prescriptions.

[0003] Extracellular vesicles (EVs) show important application prospects in the study of various diseases. Due to their stability and widespread presence in biological fluids, EVs have become important diagnostic and therapeutic biomarker carriers in liquid biopsy. For example, exosomes have been widely studied in cancer, neurodegenerative diseases, and infectious diseases, and by analyzing their molecular composition, disease mechanisms have been revealed and new diagnostic and therapeutic targets have been discovered. Microvesicles show important roles in cardiovascular and metabolic diseases, and by carrying specific proteins and RNA molecules, they participate in cell-cell communication and pathological processes. In addition, apoptotic bodies have unique applications in the study of immune regulation and cell death, and by displaying apoptotic antigens, they promote immune responses and have potential immunomodulatory effects. By analyzing the molecular composition of EVs, the mechanisms of disease occurrence and development can be revealed, and new diagnostic and therapeutic targets can be provided.

[0004] The application of multi-omics technology provides new ideas and means for the study of traditional Chinese medicine, and through genomics, transcriptomics, proteomics, and metabolomics technologies, the chemical components and biological activities of traditional Chinese medicines can be comprehensively analyzed, and their mechanisms of action at the molecular level can be revealed.

[0005] There are many problems and shortcomings in the prior art: 1) Difficulty in data integration: Traditional Chinese medicine multi-omics data includes genomics, transcriptomics, proteomics and metabolomics, etc. The data is complex and diverse, and the existing technology lacks a systematic data integration platform, making it difficult to obtain, share and analyze cross-disciplinary data. 2) Lack of innovative research methods: The existing technology has limitations in the research methods and evaluation system of the multi-component and multi-target action of traditional Chinese medicine, and it is difficult to fully reveal the pharmacodynamic mechanism of traditional Chinese medicine. 3) Insufficient standardization and quality control: The standardization and quality control of traditional Chinese medicine prescriptions have not been completely solved, and the existing technology still has deficiencies in ensuring the safety and effectiveness of its clinical application. 4) Limited data analysis technology: The existing technology has limited technical means in data analysis and interpretation, and lacks effective bioinformatics tools to process and analyze massive data, affecting the efficiency and accuracy of the research. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a traditional Chinese medicine prescription system based on multi-omics integrated analysis of extracellular vesicles, which reveals the molecular mechanism in the treatment process or identifies potential biomarkers through the study of extracellular vesicles in traditional Chinese medicine prescriptions.

[0007] To achieve the above-mentioned purpose, the present application provides the following scheme:

[0008] The traditional Chinese medicine prescription system based on multi-omics integrated analysis of extracellular vesicles comprises a data acquisition module, a data processing module, a multi-omics integration module, a discovery verification module and a systems biology application module.

[0009] The data acquisition module is used to acquire biopsy sample information of different groups of individuals based on related experiments and literature of traditional Chinese medicine prescription treatment.

[0010] The data processing module is used to obtain and preprocess the extracellular vesicle data and the gut microbiota data based on the biopsy sample information; wherein the extracellular vesicles include exosomes, microvesicles and apoptotic bodies.

[0011] The multi-omics integration module is used to obtain multi-omics data of different groups based on the preprocessed extracellular vesicle data and gut microbiota data, and integrate the multi-omics data based on different bioinformatics methods to obtain multi-omics integrated data.

[0012] The discovery verification module is used to compare and screen the multi-omics integrated data of different groups to obtain potential prescription efficacy evaluation biomarkers, and experimentally verify the potential prescription efficacy evaluation biomarkers to obtain verification results.

[0013] The system biology application module is configured to construct a gene-protein-metabolite interaction network based on system biology, the potential Chinese formula efficacy evaluation biomarker, and the verification result, and obtain a regulation mechanism of the extracellular vesicles in a biological system.

[0014] Preferably, the biopsy sample information includes liquid biopsy sample information and fecal biopsy sample information of individuals in different groups.

[0015] Preferably, the different groups of individuals include healthy normal group individuals, diseased group individuals, and traditional Chinese formula treatment group individuals; and the liquid biopsy sample information includes blood data, urine data, cerebrospinal fluid data, and saliva data.

[0016] Preferably, the data processing module is configured to perform data cleaning, missing value processing, and standardization on the extracellular vesicle data and the intestinal flora data.

[0017] Preferably, the multi-omics integration module includes:

[0018] A genomics data acquisition unit is configured to sequence DNA data of extracellular vesicles in different groups to obtain genomics data.

[0019] A transcriptomics data acquisition unit is configured to perform reverse transcription on RNA data of extracellular vesicles in different groups to obtain transcriptomics data.

[0020] A proteomics data acquisition unit is configured to extract protein data of extracellular vesicles in different groups and obtain proteomics data based on mass spectrometry technology.

[0021] A metabolomics data acquisition unit is configured to obtain metabolomics data based on nuclear magnetic resonance and liquid chromatography-mass spectrometry technology.

[0022] A metagenomics data acquisition unit is configured to obtain metagenomics data of intestinal flora in different groups based on high-throughput sequencing technology.

[0023] An integration unit is configured to integrate the genomics data, the transcriptomics data, the proteomics data, the metabolomics data, and the metagenomics data to obtain multi-omics integration data.

[0024] Preferably, the discovery verification module includes:

[0025] A gene comparison unit is configured to compare the genomics data of different groups to obtain gene variation structures.

[0026] A transcription comparison unit is configured to compare the transcriptomics data of different groups to obtain gene expression differences.

[0027] A protein comparison unit is configured to compare proteomics data of different groups to obtain protein expression differences.

[0028] A metabolic comparison unit is configured to compare metabolomics data of different groups to obtain metabolic flow differences based on existing biological information databases to construct metabolic pathway models.

[0029] A gut flora comparison unit is configured to compare metagenomics data of different groups to obtain flora composition differences.

[0030] A marker acquisition unit is configured to obtain potential traditional prescription efficacy evaluation biomarkers based on the genomic variation structure, the gene expression differences, the protein expression differences, the metabolic flow differences, and the flora composition differences.

[0031] A verification unit is configured to experimentally verify the potential traditional prescription efficacy evaluation biomarkers to obtain verification results.

[0032] Preferably, the system biology application module comprises:

[0033] A network topology analysis unit is configured to analyze nodes and functional modules of a constructed network based on the potential traditional prescription efficacy evaluation biomarkers and the verification results to obtain biological functions of key nodes and functional modules.

[0034] A functional enrichment analysis unit is configured to perform gene ontology analysis and pathway enrichment analysis on the biological functions of the key nodes and functional modules to obtain a gene-protein-metabolite interaction network.

[0035] Preferably, the network topology analysis unit comprises:

[0036] A node analysis subunit is configured to analyze node degree distribution based on the potential traditional prescription efficacy evaluation biomarkers and the verification results to obtain key nodes; wherein the key nodes comprise genes, proteins, and metabolites.

[0037] A module analysis subunit is configured to obtain functions of data acquisition modules, data processing modules, multi-omics integration modules, and discovery and verification modules based on detection algorithms and analyze biological functions inside the functional modules.

[0038] Preferably, the functional enrichment analysis unit comprises:

[0039] A gene ontology analysis subunit is configured to perform gene ontology analysis on the biological functions of the key nodes and functional modules to identify biological functions.

[0040] The pathway enrichment analysis subunit performs pathway enrichment analysis on the key nodes and each functional module, and identifies biological processes.

[0041] Compared with the prior art, the beneficial effects of the present application are: the system is used for modernization research, mechanism analysis, biomarker discovery and personalized medication guidance of traditional Chinese medicine classical prescriptions. By integrating various visual data such as genomics, transcriptomics, proteomics, metabolomics and metagenomics of intestinal flora, the system analyzes the mechanism of traditional Chinese medicine classical prescriptions at the molecular level, and further improves the clinical application effect and international popularization of traditional Chinese medicine classical prescriptions. With the support of bioinformatics, systems biology and cloud computing technology, the present application constructs an efficient and intelligent cloud platform for traditional Chinese medicine classical prescription research, which is widely used in specific technical fields such as basic research, clinical research, new drug development and personalized medicine of traditional Chinese medicine. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the present application, the following briefly introduces the drawings needed in the embodiments. Obviously, the drawings described in the following are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 The structure schematic diagram of the traditional Chinese medicine classical prescription cloud platform for analyzing extracellular vesicles based on multi-omics integration of the embodiments of the present application;

[0044] Figure 2 The protein-metabolite interaction network schematic diagram of the embodiments of the present application;

[0045] Figure 3 The protein interaction network schematic diagram of the embodiments of the present application;

[0046] Figure 4 The traditional Chinese medicine active ingredient-target-pathway signal visualization diagram of the embodiments of the present application;

[0047] Figure 5 The transcriptome-protein-metabolite correlation network diagram of the embodiments of the present application. DETAILED DESCRIPTION

[0048] The technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] In order to make the above objectives, characteristics and advantages of the present application more apparent, comprehensible and easier to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0050] Embodiment one

[0051] As shown in Figure 1 The traditional Chinese medicine formula system (cloud platform) based on multi-omics integrated analysis of extracellular vesicles includes a data acquisition module, a data processing module, a multi-omics integration module (multi-omics integration and analysis module), a discovery and verification module (biomarker discovery and verification module for traditional Chinese medicine formula efficacy evaluation), and a systems biology application module.

[0052] The data acquisition module is used to collect biopsy sample information of different groups of individuals based on related experiments and literature of traditional Chinese medicine formula treatment. Further embodiments are that the biopsy sample information includes liquid biopsy sample information and fecal biopsy sample information of different groups of individuals.

[0053] Among them, different groups of individuals include healthy normal group individuals, diseased group individuals and traditional Chinese medicine formula treatment group individuals; liquid biopsy sample information includes blood data, urine data, cerebrospinal fluid data and saliva data. EVs are extracted from these biological fluids for multi-omics analysis.

[0054] Healthy normal group: including healthy individuals as a control group for comparative analysis.

[0055] Diseased group: including patients with specific diseases for studying disease-related molecular mechanisms.

[0056] Traditional Chinese medicine formula treatment group: including patients receiving traditional Chinese medicine formula treatment for studying the therapeutic effect and molecular mechanism of traditional Chinese medicine formula.

[0057] The data processing module is used to obtain and preprocess extracellular vesicle data and gut microbiota data based on biopsy sample information; wherein the extracellular vesicles include exosomes, microvesicles and apoptotic bodies.

[0058] Exosomes: small vesicles secreted by cells, involved in intercellular communication.

[0059] Microvesicles: larger vesicles formed by budding of cell membranes.

[0060] Apoptotic bodies: small bodies formed by apoptotic cells, carrying cell apoptosis-related information.

[0061] In the present embodiment, extracellular vesicles (EVs) can be divided into subgroups such as exosomes, microvesicles and apoptotic bodies according to their size and origin. Each subgroup has important biological characteristics and functions in data processing modules, and the present application will also collect EVs related biological information data to classify and study exosomes, microvesicles and apoptotic bodies to ensure the universality and comprehensiveness of the data.

[0062] Further embodiments are that in the data processing module, the extracellular vesicle data and the intestinal flora data are subjected to data cleaning, missing value processing and standardization.

[0063] The multi-omics integration module is used to obtain multi-omics data of different groups based on the preprocessed extracellular vesicle data and intestinal flora data, and to integrate the multi-omics data based on different bioinformatics methods to obtain multi-omics integrated data.

[0064] The multi-omics analysis includes:

[0065] Genomics: Study of the structure, function and changes of the genome.

[0066] Transcriptomics: Study of the overall picture and dynamic changes of gene expression.

[0067] Proteomics: Study of the structure, function and interaction of proteins.

[0068] Metabolomics: Study of the composition and changes of metabolites.

[0069] Metagenomics: Study of the composition and function of intestinal flora.

[0070] Further embodiments are that the multi-omics integration module includes:

[0071] The genomics data acquisition unit is used to sequence the DNA data of different groups of extracellular vesicles to obtain genomics data; specifically, the genomics data are used to study the effects of traditional Chinese medicine on genome structure and gene mutation, and the data are obtained by whole genome sequencing (WGS) and whole exome sequencing (WES). First, the DNA of different groups of extracellular vesicles is extracted to construct a sequencing library. The library is sequenced using WGS or WES technology, and adapters, low-quality bases and repetitive sequences are removed.

[0072] The transcriptomics data acquisition unit is used to reverse transcribe the RNA data of different groups of extracellular vesicles to obtain transcriptomics data; specifically, the transcriptomics data are used to analyze the regulation of traditional Chinese medicine on gene expression, and the data are obtained by RNA sequencing (RNA-seq) technology. First, RNA is extracted from different groups of extracellular vesicle samples. RNA is converted to cDNA by reverse transcription.

[0073] A proteomics data acquisition unit is configured to extract protein data of extracellular vesicles in different groups and obtain proteomics data based on mass spectrometry technology. Specifically, the proteomics data are used to study the influence of traditional Chinese medicine on protein expression and post-translational modification, and the data are obtained by mass spectrometry (MS) technology. First, proteins are extracted from extracellular vesicle samples in different groups, and enzymes such as trypsin are used to digest the proteins into peptide segments. The mass spectrometry data are analyzed to determine the protein identity and modification state.

[0074] A metabolomics data acquisition unit is configured to obtain metabolomics data based on nuclear magnetic resonance and liquid chromatography-mass spectrometry technology. Specifically, the metabolomics data are used to analyze the regulation of metabolites by traditional Chinese medicine. First, data of extracellular vesicles in different groups are obtained by nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC-MS) technology. Based on databases such as KEGG or HMDB, a metabolic pathway model is constructed to analyze the metabolic flow changes.

[0075] A metagenomics data acquisition unit is configured to obtain metagenomics data of intestinal flora in different groups based on high-throughput sequencing technology. Specifically, metagenomics analysis of intestinal flora: first, metagenomics data of intestinal flora in different groups are obtained by high-throughput sequencing technology such as 16S-rRNA gene sequencing and metagenomics sequencing. Application examples of intestinal flora in traditional Chinese medicine classic prescription research: the interaction between intestinal flora and traditional Chinese medicine classic prescriptions is studied to reveal the regulation mechanism of traditional Chinese medicine on intestinal flora and the role of flora in the efficacy of traditional Chinese medicine, providing a scientific basis for personalized medicine.

[0076] Each of the above omics analysis methods can obtain rich biological information.

[0077] An integration unit is configured to integrate genomics data, transcriptomics data, proteomics data, metabolomics data, and metagenomics data to obtain multi-omics integrated data.

[0078] A discovery verification module is configured to compare and screen multi-omics integrated data in different groups to obtain potential classic prescription efficacy evaluation biomarkers, and to experimentally verify the potential classic prescription efficacy evaluation biomarkers to obtain verification results. Specifically, by multi-omics data and bioinformatics analysis, the normal group, the patient group, and the classic prescription treatment group are compared and screened to obtain potential classic prescription efficacy evaluation biomarkers, and the significance and reliability of the biomarkers are further determined by integrated analysis.

[0079] Further embodiments are directed to the discovery verification module comprising:

[0080] A gene comparison unit is configured to compare different sets of genomics data to obtain genomic variation structure. Specifically, sequence reads are aligned to a reference genome to determine genomic variation.

[0081] A transcription comparison unit is configured to compare different sets of transcriptomics data to obtain gene expression difference. Specifically, expression differences under different conditions are compared to find potential biomarkers or regulatory mechanisms.

[0082] A protein comparison unit is configured to compare different sets of proteomics data to obtain protein expression difference. Specifically, protein function annotation, network analysis and differential expression analysis are performed.

[0083] A metabolism comparison unit is configured to compare different sets of metabolomics data, and based on existing biological information databases, construct a metabolic pathway model to obtain metabolic flow difference.

[0084] A gut microbiota comparison unit is configured to compare different sets of metagenomics data to obtain microbiota composition difference. Gut microbiota data are cleaned, assembled and annotated to analyze the composition and function of the microbiota.

[0085] A marker acquisition unit is configured to obtain potential traditional prescription efficacy evaluation biomarkers based on genomic variation structure, gene expression difference, protein expression difference, metabolic flow difference and microbiota composition difference.

[0086] A verification unit is configured to experimentally verify the potential traditional prescription efficacy evaluation biomarkers to obtain verification results.

[0087] In this embodiment, the potential traditional prescription efficacy evaluation biomarkers are verified by experiments, including qPCR, Western Blot, ELISA and other techniques, to ensure the specificity and sensitivity of the markers.

[0088] Specifically, cell lines or animal models are used for experimental verification to confirm that the screened biomarkers are indeed related to the efficacy of traditional prescriptions under experimental conditions. Samples are processed and biomarkers are detected (such as qPCR, Western Blot, etc.). The experimental results are analyzed to confirm whether the expression level of the biomarker is related to the efficacy. The role of EVs in traditional Chinese medicine prescriptions is studied to discover and verify molecules in EVs as biomarkers for traditional prescription efficacy evaluation, providing scientific basis for the efficacy of traditional Chinese medicine and personalized medication.

[0089] The system biology application module is configured to evaluate biomarkers and verify results based on system biology and potential classical prescription efficacy, construct a gene-protein-metabolite interaction network, and obtain a regulation mechanism of the extracellular vesicle in a biological system. The gene-protein-metabolite interaction network is configured to utilize a coding relationship between genes and proteins to construct a correlation between the genes and the proteins. As shown in FIGS. 8, 9, and 10, a red circle represents a metabolite, a green circle represents a protein, a line thickness represents a correlation strength between the metabolite and the protein, and a thicker line represents a stronger correlation. Figure 2 、 Figure 3 Figure 2 In the above, a red circle represents a metabolite, a green circle represents a protein, and a line thickness represents a correlation strength between the metabolite and the protein. A thicker line represents a stronger correlation.

[0090] Further embodiments are configured such that the system biology application module includes:

[0091] A network topology analysis unit is configured to analyze nodes and functional modules of the constructed network based on biomarkers and verification results of potential classical prescription efficacy, and obtain biological functions of key nodes and the functional modules.

[0092] A functional enrichment analysis unit is configured to perform gene ontology analysis and pathway enrichment analysis on the biological functions of the key nodes and the functional modules, and obtain a gene-protein-metabolite interaction network.

[0093] Further embodiments are configured such that the network topology analysis unit includes:

[0094] A node analysis subunit is configured to analyze node degree distribution based on biomarkers and verification results of potential classical prescription efficacy, and obtain key nodes. The key nodes include genes, proteins, and metabolites.

[0095] A module analysis subunit is configured to obtain functions of the data acquisition module, the data processing module, the multi-omics integration module, and the discovery and verification module based on a detection algorithm (such as MCL and Louvain), and analyze biological functions inside the functional modules.

[0096] Further embodiments are configured such that the functional enrichment analysis unit includes:

[0097] A gene ontology (GO) analysis subunit is configured to perform gene ontology analysis on the biological functions of the key nodes and the functional modules, and identify biological functions.

[0098] A pathway enrichment analysis subunit is configured to perform pathway enrichment analysis on the key nodes and the functional modules using a pathway database such as KEGG and Reactome, and identify biological processes.

[0099] In this embodiment, the specific process of constructing the gene-protein-metabolite interaction network includes:

[0100] ​Data preprocessing: Standardization and normalization of multi-omics datasets, imputation of missing values using random forest.

[0101] Exploratory data analysis: Principal component analysis (PCA) on pre-processed multi-omics datasets to identify patterns and trends in the data. Cluster analysis is used to explore similarities and differences between samples.

[0102] Model construction: Using LASSO (Least Absolute Shrinkage and Selection Operator) regression algorithm, based on metabolomics, proteomics, transcriptomics, genomics and combined (integrated data) of these omics, to predict EVs of individuals and obtain prediction models.

[0103] Feature selection: LASSO algorithm is used to handle multicollinearity problem and select important features (metabolites, proteins and genes) in the prediction model, which have strong correlation with EVs.

[0104] Model evaluation: Use independent test set to evaluate the accuracy, sensitivity and specificity of the model to test the generalization ability of the model. Confusion matrix, ROC curve and AUC value can be calculated.

[0105] Heterogeneity analysis: By comparing the differences between actual EVs and predicted EVs predicted by the prediction model, heterogeneity analysis is performed.

[0106] Bioinformatics analysis: Interpret the prediction results of the prediction model and convert the data into biological significance. Perform functional annotation and pathway analysis on the selected markers. Use databases such as KEGG and Reactome for enrichment analysis.

[0107] Knowledge base network construction and analysis: Integrate the analysis results into the knowledge base of systems biology to provide resources for further research and application. As shown in Figure 4 、 Figure 5 , use network visualization tools to construct gene-protein-metabolite interaction networks, such as Cytoscape, R packages such as igraph or circlize, to create network graphs. Set the properties of nodes and edges, such as size, color and shape, to represent different biological entities and their relationships. Add annotations such as gene names, protein functions and metabolite types to the network graph. Apply network analysis tools to identify key hub molecules or modules.

[0108] In this embodiment, the role of systems biology combined with EVs multi-omics analysis:

[0109] 1) Application of systems biology methods in EVs analysis: Apply systems biology methods to construct and analyze gene-protein-metabolite interaction networks to reveal the regulatory mechanisms in complex biological systems.

[0110] 2) Construction of gene-protein-metabolite interaction networks: Utilize systems biology tools and algorithms to construct gene-protein-metabolite interaction networks. By analyzing these networks, we can reveal the regulatory mechanisms of EVs in different physiological and pathological states, helping to discover key regulatory nodes and molecules.

[0111] 3) Analysis of EVs regulatory mechanisms in complex biological systems: Through multi-omics data and systems biology methods, we analyze the regulatory mechanisms of EVs in complex biological systems. We focus on the role of EVs in intercellular communication, metabolic regulation, and disease development, providing new theoretical basis and research ideas.

[0112] Through the construction of this platform, we can systematically and comprehensively study the molecular mechanisms of traditional Chinese medicine prescriptions in the treatment process, discover new biomarkers and therapeutic targets, and improve the clinical application effect and international promotion of traditional Chinese medicine prescriptions. The system has wide application fields:

[0113] 1) Basic research of traditional Chinese medicine: Utilize multi-omics technology to deeply study the pharmacodynamic material basis and mechanism of action of traditional Chinese medicine prescriptions, and reveal the molecular mechanism in the treatment of diseases.

[0114] 2) Clinical research and application: Through the integration and analysis of multi-omics data, we can discover potential diagnostic and therapeutic biomarkers and therapeutic targets, guide clinicians to conduct personalized medication, and improve the clinical efficacy of traditional Chinese medicine prescriptions.

[0115] 3) New drug development: Utilize the data analysis and model prediction capabilities of the platform to screen and verify new drug molecules and combinations, providing scientific basis and technical support for the development of new traditional Chinese medicine drugs.

[0116] 4) Personalized medicine: Combine the multi-omics data of patients to develop personalized traditional Chinese medicine treatment plans, achieve precision medicine, and improve the treatment effect and quality of life of patients.

[0117] 5) International promotion: Through the scientific data and analysis results of the platform, promote the recognition and application of traditional Chinese medicine prescriptions in the international medical community, and promote the promotion and development of traditional Chinese medicine in the global range.

[0118] The above-described embodiments are only descriptions of the preferred modes of the present application and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A traditional Chinese medicine formula system for analyzing extracellular vesicles based on multi-omics integrated analysis, characterized in that, The application relates to a traditional Chinese medicine (TCM) formula efficacy evaluation system based on multi-omics data integration and a method thereof. The system comprises a data collection module, a data processing module, a multi-omics integration module, a discovery verification module and a systems biology application module. The data collection module is used for collecting biopsy sample information of individuals in different groups based on related experiments and literature of TCM formula treatment. The data processing module is used for obtaining and preprocessing extracellular vesicle data and intestinal flora data based on the biopsy sample information, wherein the extracellular vesicle comprises an exosome, a microvesicle and an apoptotic body. The multi-omics integration module is used for obtaining multi-omics data of different groups based on the preprocessed extracellular vesicle data and intestinal flora data, and integrating the multi-omics data based on different bioinformatics methods to obtain multi-omics integrated data. The discovery verification module is used for comparing and screening the multi-omics integrated data of different groups to obtain potential TCM formula efficacy evaluation biomarkers, and experimentally verifying the potential TCM formula efficacy evaluation biomarkers to obtain verification results. The systems biology application module is used for constructing a gene-protein-metabolite interaction network based on systems biology, the potential TCM formula efficacy evaluation biomarkers and the verification results, and obtaining a regulation mechanism of extracellular vesicles in a biological system.

2. The traditional Chinese medicine formula system based on multi-omics integrated analysis of extracellular vesicles according to claim 1, characterized in that, The biopsy sample information comprises liquid biopsy sample information and fecal biopsy sample information of individuals in different groups. The individuals in different groups comprise healthy normal group individuals, diseased group individuals and TCM formula treatment group individuals, and the liquid biopsy sample information comprises blood data, urine data, cerebrospinal fluid data and saliva data.

3. The traditional Chinese medicine formula system based on multi-omics integrated analysis of extracellular vesicles according to claim 2, characterized in that, In the data processing module, the extracellular vesicle data and the intestinal flora data are subjected to data cleaning, missing value processing and standardization.

4. The traditional Chinese medicine formula system based on multi-omics integrated analysis of extracellular vesicles according to claim 2, characterized in that, The multi-omics integration module comprises: a genomics data acquisition unit used for sequencing DNA data of extracellular vesicles of different groups to obtain genomics data; a transcriptomics data acquisition unit used for reverse transcribing RNA data of extracellular vesicles of different groups to obtain transcriptomics data; a proteomics data acquisition unit used for extracting protein data of extracellular vesicles of different groups and obtaining proteomics data based on mass spectrometry technology; a metabolomics data acquisition unit used for obtaining metabolomics data based on nuclear magnetic resonance and liquid chromatography-mass spectrometry technology; a metagenomics data acquisition unit used for obtaining metagenomics data of intestinal flora of different groups based on high-throughput sequencing technology; and an integration unit used for integrating the genomics data, the transcriptomics data, the proteomics data, the metabolomics data and the metagenomics data to obtain multi-omics integrated data.

5. The traditional Chinese medicine formula system based on multi-omics integrated analysis of extracellular vesicles according to claim 4, characterized in that, The discovery verification module comprises: a gene comparison unit used for comparing genomics data of different groups to obtain gene variation structures; a transcription comparison unit used for comparing transcriptomics data of different groups to obtain gene expression differences; a protein comparison unit used for comparing proteomics data of different groups to obtain protein expression differences; and a metabolomics comparison unit used for comparing metabolomics data of different groups to obtain metabolite expression differences. A metabolic comparison unit is configured to compare metabolomics data of different groups, and construct a metabolic pathway model based on an existing biological information database to obtain metabolic flow differences. A gut flora comparison unit is configured to compare metagenomics data of different groups to obtain flora composition differences. A marker obtaining unit is configured to obtain potential traditional prescription efficacy evaluation biomarkers based on the genomic variation structure, the gene expression differences, the protein expression differences, the metabolic flow differences, and the flora composition differences. A verification unit is configured to experimentally verify the potential traditional prescription efficacy evaluation biomarkers to obtain verification results.

6. The traditional Chinese medicine formula system based on multi-omics integrated analysis of extracellular vesicles according to claim 5, characterized in that, The system biology application module includes: A network topology analysis unit is configured to analyze nodes and functional modules of a constructed network based on the potential traditional prescription efficacy evaluation biomarkers and the verification results to obtain biological functions of key nodes and functional modules. A functional enrichment analysis unit is configured to perform gene ontology analysis and pathway enrichment analysis on the biological functions of the key nodes and functional modules to obtain a gene-protein-metabolite interaction network.

7. The traditional Chinese medicine formula system based on multi-omics integrated analysis of extracellular vesicles according to claim 6, characterized in that, The network topology analysis unit includes: A node analysis subunit is configured to analyze node degree distribution based on the potential traditional prescription efficacy evaluation biomarkers and the verification results to obtain key nodes, wherein the key nodes include genes, proteins, and metabolites. A module analysis subunit is configured to obtain functions of data acquisition modules, data processing modules, multi-omics integration modules, and discovery and verification modules based on a detection algorithm, and analyze biological functions inside each functional module.

8. The traditional Chinese medicine formula system based on multi-omics integrated analysis of extracellular vesicles according to claim 6, characterized in that, The functional enrichment analysis unit includes: A gene ontology analysis subunit is configured to perform gene ontology analysis on the biological functions of the key nodes and functional modules to identify biological functions. A pathway enrichment analysis subunit is configured to perform pathway enrichment analysis on the key nodes and functional modules to identify biological processes.

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